How Do Restaurants Get Found in AI Search Results?
Schema markup for restaurants packages start at just £49 and are implemented in 48 hours. We translate your full menu, dietary options, booking links, and cuisine type into machine-readable code, ensuring AI assistants recommend you for the specific dining experiences you offer.
Why Restaurants Are Missing Out on AI Visibility
The PDF Menu Problem
This is the biggest killer of restaurant SEO. AI cannot easily read a PDF menu. If your "Vegan Lasagne" is trapped in a PDF image, ChatGPT doesn't know you serve it. We use Menu schema to turn your dishes into structured data. This means when someone asks for "gluten-free pasta near me," AI can actually "read" your menu and recommend you.
Dietary Requirement Blindness
Modern diners use AI to filter by diet. "Find me a halal steakhouse" or "dairy-free dessert spots." If your dietary suitability isn't marked up in schema, you are filtered out. We explicitly tag your restaurant and menu items with suitableForDiet properties, making you visible to these niche but loyal customer bases.
Booking Friction
AI assistants want to provide solutions, not just lists. They favour restaurants where they can find a booking link. We use ReserveAction schema to link your listing directly to your OpenTable, ResDiary, or direct booking page. This reduces friction and signals to the AI that you are "open for business."
Atmosphere & Occasion
People search by vibe: "quiet restaurant for business lunch" or "lively brunch spot." While harder to capture, we use servesCuisine and price range schema to give AI clues about your style. A "Gastropub" schema tells a different story to a "FineDining" schema, helping match you to the right occasion.
How AI Visible Helps Restaurants Be Seen
We audit your online presence to digitise your menu data and identify your key selling points (e.g., "Dog Friendly," "Rooftop Bar"). We implement Organization schema configured as Restaurant. We use Menu schema to itemise your starters, mains, and desserts. We tag your cuisine types (Italian, Indian, Fusion) and price range. Finally, we use AggregateRating schema to showcase your diner reviews.
📊 What You Get:
Restaurant Organization schema with cuisine tags • Menu schema for key dishes • Dietary requirement tagging (Vegan, GF, Halal) • Reservation system integration • AggregateRating schema for reviews.
48-Hour Implementation Process for Restaurants
We know hospitality is fast-paced. Our process is designed to be invisible to your operations, following our New Rules of SEO 2026 framework. Day 1 involves a digital audit where we scrape your menu data, opening hours, and booking links. We map this data to the restaurant-specific schema vocabulary.
On Day 2, we generate the JSON-LD code, validating it against Google's structured data guidelines and testing it with our internal AI prompts ("Find a kid-friendly burger place in [City]"). Once verified, we deploy it to your site. In the following weeks, we monitor your visibility for dish-specific searches like "best Sunday roast" or "truffle pasta".
Essential Schema Types for Restaurants
Organization Schema: Your Digital Shopfront
Configured as Restaurant (or subtypes like CafeOrCoffeeShop), this schema establishes your identity. We include your logo, price range (e.g., ££), and cuisine type. This is the baseline data AI needs to categorise you correctly.
Menu Schema: Unlocking Your Food
We use Menu schema to break your menu down into Sections (Starters, Mains) and MenuItems. Each item can have a name, description, price, and dietary tags. This is the secret weapon for ranking for specific food queries like "best fish and chips".
Reservation Schema: Driving Covers
We use potentialAction with a ReserveAction to tell AI exactly where to send users to book a table. Whether you use a third-party widget or a simple contact form, we make the pathway clear for the AI assistant.
Review Schema: Social Proof
Diners trust other diners. We ensure your 4.5+ star Google or TripAdvisor reviews are visible to AI engines using AggregateRating schema. When a hungry user sees those stars next to your name in an AI answer, click-through rates skyrocket.
Typical Implementation Example
The following is an illustrative example based on common outcomes from schema markup implementations. Individual results may vary.
The Scenario: A local Italian restaurant had empty tables on weeknights despite great food. Competitors appeared in 'best pasta' lists.
The Diagnosis: Their menu was a PDF. AI couldn't read 'Gluten-Free Pasta' or 'Vegan Pizza' options.
The Solution: We added Restaurant schema with `hasMenu` and `servesCuisine` properties to make dishes machine-readable.
The Outcome: Reservation intent from local search **increased by 20%**, with a measurable uptick in searches for specific dietary requirements. (Source: Restaurant Association Data)